Update ML Intern artifact metadata

README.md

@@ -1,13 +1,14 @@
 ---
 license: cc-by-4.0
 tags:
-  - medical-imaging
-  - ultrasound
-  - thyroid
-  - classification
-  - efficientnet
+- medical-imaging
+- ultrasound
+- thyroid
+- classification
+- efficientnet
+- ml-intern
 datasets:
-  - Johnyquest7/TN5000-thyroid-nodule-classification
+- Johnyquest7/TN5000-thyroid-nodule-classification
 ---
 # Thyroid Nodule Classification - EfficientNetV2-S (AUC-Optimized v4)
 Frozen backbone, deeper head with Dropout 0.5. Optimized for AUC-ROC.
@@ -22,3 +23,23 @@ Frozen backbone, deeper head with Dropout 0.5. Optimized for AUC-ROC.
 | AUC-ROC | 0.6835 | [0.6467, 0.7199] |
 ## Citation
 Yu, Xiaoxian et al. "TN5000..." Scientific Data (Nature), 2025.
+
+<!-- ml-intern-provenance -->
+## Generated by ML Intern
+
+This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.
+
+- Try ML Intern: https://smolagents-ml-intern.hf.space
+- Source code: https://github.com/huggingface/ml-intern
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = 'Johnyquest7/Thyroid_EfficientNetV2'
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+```
+
+For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.